cluster = parallel::makeCluster(rep("localhost", parallel::detectCores()), type = "SOCK")
experiment = Rtrack::read_experiment("Experiment_description2.xlsx", format = "Excel", cluster = cluster)
## Warning in read.table(file = file, header = header, sep = sep, quote = quote, :
## unvollstädige letzte Zeile von readTableHeader in './Arena_RV.txt' gefunden
#Closing multicore cluster to reduce system load
parallel::stopCluster(cluster)
These metrics can be compared and extracted from the pure track files.
experiment$summary.variables
## [1] "path.length" "mean.velocity"
## [3] "sd.velocity" "total.time"
## [5] "latency.to.goal" "goal.crossings"
## [7] "old.goal.crossings" "coverage"
## [9] "mean.d.centroid" "mean.d.goal"
## [11] "mean.d.old.goal" "mean.d.origin"
## [13] "sd.d.centroid" "sd.d.goal"
## [15] "sd.d.old.goal" "sd.d.origin"
## [17] "centroid.goal.displacement" "centroid.old.goal.displacement"
## [19] "mean.initial.heading.error" "initial.trajectory.error"
## [21] "initial.reversal.error" "turning"
## [23] "turning.absolute" "efficiency"
## [25] "roaming.entropy" "time.in.zone.pool"
## [27] "time.in.zone.wall" "time.in.zone.far.wall"
## [29] "time.in.zone.annulus" "time.in.zone.goal"
## [31] "time.in.zone.old.goal" "time.in.zone.n.quadrant"
## [33] "time.in.zone.e.quadrant" "time.in.zone.s.quadrant"
## [35] "time.in.zone.w.quadrant"
par(mfrow = c(2, 2))
Rtrack::plot_variable("path.length", experiment = experiment, factor = "Strain", exclude.probe = TRUE,lwd = 2)
Rtrack::plot_variable("path.length", experiment = experiment, factor = "Age_group", exclude.probe = TRUE,lwd = 2)
Rtrack::plot_variable("path.length", experiment = experiment, factor = "Housing", exclude.probe = TRUE,lwd = 2)
Rtrack::plot_variable("path.length", experiment = experiment, factor = "All", exclude.probe = TRUE,lwd = 2)
Note that the probe trials have been omitted from these plots.
back to top *** ## Heatmap {#anchor3}
wt.metrics = experiment$metrics[experiment$factors$Strain == "WT" &
(experiment$factors$`_Day` == 1 | experiment$factors$`_Day` == 2 | experiment$factors$`_Day` == 3 | experiment$factors$`_Day` == 4|
experiment$factors$`_Day` == 5| experiment$factors$`_Day` == 6)]
dTg.metrics = experiment$metrics[experiment$factors$Strain == "dTg" &
(experiment$factors$`_Day` == 1 | experiment$factors$`_Day` == 2 | experiment$factors$`_Day` == 3 | experiment$factors$`_Day` == 4| experiment$factors$`_Day` == 5| experiment$factors$`_Day` == 6)]
APP.metrics = experiment$metrics[experiment$factors$Strain == "APPswe" &
(experiment$factors$`_Day` == 1 | experiment$factors$`_Day` == 2 | experiment$factors$`_Day` == 3 | experiment$factors$`_Day` == 4| experiment$factors$`_Day` == 5| experiment$factors$`_Day` == 6)]
PS1.metrics = experiment$metrics[experiment$factors$Strain == "PS1dE9" &
(experiment$factors$`_Day` == 1 | experiment$factors$`_Day` == 2 | experiment$factors$`_Day` == 3 | experiment$factors$`_Day` == 4| experiment$factors$`_Day` == 5| experiment$factors$`_Day` == 6)]
par(mfrow = c(2, 2))
Rtrack::plot_density(wt.metrics, title = "wt Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTg.metrics, title = "dTg Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APP.metrics, title = "APPswe Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1.metrics, title = "PS1dE9 Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
back to top *** ## Heatmap_reversal {#anchor4}
wtr.metrics = experiment$metrics[experiment$factors$Strain == "WT" &
(experiment$factors$`_Day` == 7 | experiment$factors$`_Day` == 8 | experiment$factors$`_Day` == 9 | experiment$factors$`_Day` == 10)]
dTgr.metrics = experiment$metrics[experiment$factors$Strain == "dTg" &
(experiment$factors$`_Day` == 7 | experiment$factors$`_Day` == 8 | experiment$factors$`_Day` == 9 | experiment$factors$`_Day` == 10)]
APPr.metrics = experiment$metrics[experiment$factors$Strain == "APPswe" &
(experiment$factors$`_Day` ==7 | experiment$factors$`_Day` == 8 | experiment$factors$`_Day` == 9 | experiment$factors$`_Day` == 10)]
PS1r.metrics = experiment$metrics[experiment$factors$Strain == "PS1dE9" &
(experiment$factors$`_Day` == 7 | experiment$factors$`_Day` == 8 | experiment$factors$`_Day` == 9 | experiment$factors$`_Day` == 10)]
par(mfrow = c(2, 2))
Rtrack::plot_density(wtr.metrics, title = "WT_reversal Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgr.metrics, title = "dTg_reversal Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPr.metrics, title = "APPswe_reversal Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1r.metrics, title = "PS1dE9_reversal Heatmap",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
back to top *** ## Calling strategies {#anchor5}
strategies = Rtrack::call_strategy(experiment$metrics)
limits called strategies to those, where confidence is greater than 40%
dim(Rtrack::threshold_strategies(strategies, 0.4)$calls)
## [1] 4203 12
back to top *** ## Plotting strategies of all age groups combined {#anchor7}
par(mfrow = c(2, 2))
Rtrack::plot_strategies(strategies, experiment = experiment, factor = "Strain",
exclude.probe = TRUE)
back to top *** ## Plotting thresholded strategies of all age groups combined {#anchor8}
par(mfrow = c(2, 2))
Rtrack::plot_strategies(Rtrack::threshold_strategies(strategies, 0.4), experiment = experiment,
factor = "Strain", exclude.probe = TRUE)
back to top *** ## Saving the results {#anchor9} Here we export the results of the analyzed Track Files into a data.frame, to analyse them further.
results = Rtrack::export_results(experiment)
datatable(results, rownames = FALSE, filter="top", options = list(pageLength = 5, scrollX=T) )
## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html
back to top *** ## Plotting with ggplot2 {#anchor10}
library(ggplot2)
library(readxl)
Rtrack::export_results(experiment, file = "Results3.xlsx")
Results=read_excel("Results3.xlsx")
ggplot(Results, aes(x=`_Day`,y=path.length,color=factor(Strain)))+geom_jitter()
Results
## # A tibble: 5,960 x 60
## Track_ID `_TargetID` `_Day` `_Trial` `_Arena` Condition Probe Trial Strain
## <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 Track_4 60-911 1 1 Arena_SW.t~ WT_STD FALSE 4 WT
## 2 Track_8 60-293 1 1 Arena_SW.t~ WT_STD FALSE 8 WT
## 3 Track_9 60-296 1 1 Arena_SW.t~ WT_STD FALSE 9 WT
## 4 Track_10 60-333 1 1 Arena_SW.t~ dTg_STD FALSE 10 dTg
## 5 Track_14 60-911 1 2 Arena_SW.t~ WT_STD FALSE 14 WT
## 6 Track_18 60-293 1 2 Arena_SW.t~ WT_STD FALSE 18 WT
## 7 Track_19 60-296 1 2 Arena_SW.t~ WT_STD FALSE 19 WT
## 8 Track_20 60-333 1 2 Arena_SW.t~ dTg_STD FALSE 20 dTg
## 9 Track_24 60-911 1 3 Arena_SW.t~ WT_STD FALSE 24 WT
## 10 Track_28 60-293 1 3 Arena_SW.t~ WT_STD FALSE 28 WT
## # ... with 5,950 more rows, and 51 more variables: Housing <chr>,
## # Age_group <chr>, Age_months <chr>, All <chr>, strategy <dbl>, name <chr>,
## # confidence <dbl>, 1 <dbl>, 2 <dbl>, 3 <dbl>, 4 <dbl>, 5 <dbl>, 6 <dbl>,
## # 7 <dbl>, 8 <dbl>, 9 <dbl>, path.length <dbl>, mean.velocity <dbl>,
## # sd.velocity <dbl>, total.time <dbl>, latency.to.goal <dbl>,
## # goal.crossings <dbl>, old.goal.crossings <dbl>, coverage <dbl>,
## # mean.d.centroid <dbl>, mean.d.goal <dbl>, mean.d.old.goal <lgl>,
## # mean.d.origin <dbl>, sd.d.centroid <dbl>, sd.d.goal <dbl>,
## # sd.d.old.goal <lgl>, sd.d.origin <dbl>, centroid.goal.displacement <dbl>,
## # centroid.old.goal.displacement <lgl>, mean.initial.heading.error <dbl>,
## # initial.trajectory.error <dbl>, initial.reversal.error <lgl>,
## # turning <dbl>, turning.absolute <dbl>, efficiency <dbl>,
## # roaming.entropy <dbl>, time.in.zone.pool <dbl>, time.in.zone.wall <dbl>,
## # time.in.zone.far.wall <dbl>, time.in.zone.annulus <dbl>,
## # time.in.zone.goal <dbl>, time.in.zone.old.goal <dbl>,
## # time.in.zone.n.quadrant <dbl>, time.in.zone.e.quadrant <dbl>,
## # time.in.zone.s.quadrant <dbl>, time.in.zone.w.quadrant <dbl>
WT_STD=filter(Results, Strain=='WT'&Housing=='STD')
dTg_STD=filter(Results, Strain=='dTg'&Housing=='STD')
APP_STD=filter(Results, Strain=='APPswe'&Housing=='STD')
PS_STD=filter(Results, Strain=='PS1dE9'&Housing=='STD')
WT_ENR=filter(Results, Strain=='WT'&Housing=='ENR')
dTg_ENR=filter(Results, Strain=='dTg'&Housing=='ENR')
APP_ENR=filter(Results, Strain=='APPswe'&Housing=='ENR')
PS_ENR=filter(Results, Strain=='PS1dE9'&Housing=='ENR')
par(mfrow = c(2, 4))
WT_STD %>%
mutate(WT_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length WT STD")+scale_fill_jco()
dTg_STD %>%
mutate(dTg_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length dTg STD")+scale_fill_jco()
APP_STD %>%
mutate(APP_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length APPswe1 STD")+scale_fill_jco()
PS_STD %>%
mutate(PS_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length PS1dE9 STD")+scale_fill_jco()
WT_ENR %>%
mutate(WT_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length WT ENR")+scale_fill_jco()
dTg_ENR %>%
mutate(dTg_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length dTg ENR")+scale_fill_jco()
APP_ENR %>%
mutate(APP_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length APPswe1 ENR")+scale_fill_jco()
PS_ENR %>%
mutate(PS_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length PS1dE9 ENR")+scale_fill_jco()+
facet_wrap(~Age_group)
back to top *** ## Latency Graphs{#anchor13}
WT_STD %>%
mutate(WT_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s WT STD")+scale_fill_jco()
## Warning: Removed 587 rows containing non-finite values (stat_boxplot).
dTg_STD %>%
mutate(dTg_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s dTg STD")+scale_fill_jco()
## Warning: Removed 494 rows containing non-finite values (stat_boxplot).
APP_STD %>%
mutate(APP_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s APPswe1 STD")+scale_fill_jco()
## Warning: Removed 315 rows containing non-finite values (stat_boxplot).
PS_STD %>%
mutate(PS_STD, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s PS1dE9 STD")+scale_fill_jco()
## Warning: Removed 285 rows containing non-finite values (stat_boxplot).
WT_ENR %>%
mutate(WT_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s WT ENR")+scale_fill_jco()
## Warning: Removed 476 rows containing non-finite values (stat_boxplot).
dTg_ENR %>%
mutate(dTg_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s dTg ENR")+scale_fill_jco()
## Warning: Removed 542 rows containing non-finite values (stat_boxplot).
APP_ENR %>%
mutate(APP_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s APPswe1 ENR")+scale_fill_jco()
## Warning: Removed 262 rows containing non-finite values (stat_boxplot).
PS_ENR %>%
mutate(PS_ENR, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=latency.to.goal, fill=Age_group))+geom_boxplot()+
labs(x="Day",
y="Average Latency in s",
title="Mean Latency in s PS1dE9 ENR")+scale_fill_jco()+
facet_wrap(~Age_group)
## Warning: Removed 242 rows containing non-finite values (stat_boxplot).
back to top *** ## UPDATED Heatmap_probe {#anchor14}
par(mfrow = c(2, 2))
Rtrack::plot_density(wtp.metrics, title = "WT Probe Heatmap 3-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgp.metrics, title = "dTg Probe Heatmap 3-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPp.metrics, title = "APPswe Probe Heatmap 3-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1p.metrics, title = "PS1dE9 Probe Heatmap 3-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
par(mfrow = c(2, 4))
Rtrack::plot_density(wtpstd.metrics, title = "WT Probe Heatmap STD 3-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpstd.metrics, title = "dTg Probe Heatmap STD 3-25mo", col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPpstd.metrics, title = "APPswe Probe Heatmap STD 3-25mo", col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1pstd.metrics, title = "PS1dE9 Probe Heatmap STD 3-25mo", col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(wtpenr.metrics, title = "WT Probe Heatmap ENR 3-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpenr.metrics, title = "dTg Probe Heatmap ENR 3-25mo", col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPpenr.metrics, title = "APPswe Probe Heatmap ENR 3-25mo", col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1penr.metrics, title = "PS1dE9 Probe Heatmap ENR 3-25mo", col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
WT -> dTg -> APPswe -> PS1dE9 back to top
par(mfrow = c(1, 3))
###########WT_Probe##########
Rtrack::plot_density(wtpstd3.metrics, title = "WT Probe Heatmap STD 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(wtpstd14.metrics, title = "WT Probe Heatmap STD 13-14mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(wtpstd25.metrics, title = "WT Probe Heatmap STD 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(wtpenr3.metrics, title = "WT Probe Heatmap ENR 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(wtpenr14.metrics, title = "WT Probe Heatmap ENR 13-14mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(wtpenr25.metrics, title = "WT Probe Heatmap ENR 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
###########dTg_Probe##########
Rtrack::plot_density(dTgpstd3.metrics, title = "dTg Probe Heatmap STD 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpstd14.metrics, title = "dTg Probe Heatmap STD 13-14mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpstd25.metrics, title = "dTg Probe Heatmap STD 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpenr3.metrics, title = "dTg Probe Heatmap ENR 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpenr14.metrics, title = "dTg Probe Heatmap ENR 13-14mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(dTgpenr25.metrics, title = "dTg Probe Heatmap ENR 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
###########APP_Probe##########
Rtrack::plot_density(APPpstd3.metrics, title = "APPswe Probe Heatmap STD 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
#Rtrack::plot_density(APPpstd14.metrics, title = "APPswe Probe Heatmap STD 13-14mo",
# col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPpstd25.metrics, title = "APPswe Probe Heatmap STD 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPpenr3.metrics, title = "APPswe Probe Heatmap ENR 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
#Rtrack::plot_density(APPpenr14.metrics, title = "APPswe Probe Heatmap ENR 13-14mo",
# col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(APPpenr25.metrics, title = "APPswe Probe Heatmap ENR 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
###########PS1_Probe##########
Rtrack::plot_density(PS1pstd3.metrics, title = "PS1dE9 Probe Heatmap STD 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
#Rtrack::plot_density(PS1pstd14.metrics, title = "PS1dE9 Probe Heatmap STD 13-14mo",
# col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1pstd25.metrics, title = "PS1dE9 Probe Heatmap STD 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1penr3.metrics, title = "PS1dE9 Probe Heatmap ENR 3mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
#Rtrack::plot_density(PS1penr14.metrics, title = "PS1dE9 Probe Heatmap ENR 13-14mo",
# col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
Rtrack::plot_density(PS1penr25.metrics, title = "PS1dE9 Probe Heatmap ENR 17-25mo",
col = colorRampPalette(c("#000C9E", "#00FEF6", "#FE009E"))(100))
back to top *** ## UPDATED Path Length Graphs {#anchor15}
mo3=filter(Results, Age_group=='3')
mo14=filter(Results, Age_group=='13-14')
mo25=filter(Results, Age_group=='17-25')
mo3 %>%
mutate(mo3, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Condition))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length 3mo mice")+scale_fill_manual(values = c("dTg_ENR" = "#ad5fc9", "dTg_STD" = "#bc91cc","WT_ENR" = "#6eca64", "WT_STD" = "#98cc93","APPswe_ENR" = "#d6564b", "APPswe_STD" = "#db867f","PS1dE9_ENR" = "#918730", "PS1dE9_STD" = "#c5b740"))
mo14 %>%
mutate(mo14, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Condition))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length 13-14mo mice")+scale_fill_manual(values = c("dTg_ENR" = "#ad5fc9", "dTg_STD" = "#bc91cc","WT_ENR" = "#6eca64", "WT_STD" = "#98cc93","APPswe_ENR" = "#d6564b", "APPswe_STD" = "#db867f","PS1dE9_ENR" = "#918730", "PS1dE9_STD" = "#c5b740"))
mo25 %>%
mutate(mo25, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=path.length, fill=Condition))+geom_boxplot()+
labs(x="Day",
y="Average Path length",
title="Mean Path length 17-25mo mice")+scale_fill_manual(values = c("dTg_ENR" = "#6c7ed7", "dTg_STD" = "#909ef3","WT_ENR" = "#9f48a3", "WT_STD" = "#ce73cf","APPswe_ENR" = "#c85632", "APPswe_STD" = "#e9724b","PS1dE9_ENR" = "#9f9201", "PS1dE9_STD" = "#cab95b"))
Boxplot explanation:
Middle line in box -> Median
Box -> shows middle 50% of data(Distance between 1. and 3. Quartil)
Whisker(vertikal lines) -> show upper/lower 25% of data w/o outliers
Points -> outlier
*** ## UPDATED Velocity (Mean) Graphs {#anchor16}
mo3=filter(Results, Age_group=='3')
mo14=filter(Results, Age_group=='13-14')
mo25=filter(Results, Age_group=='17-25')
mo3 %>%
mutate(mo3, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=mean.velocity, fill=Condition))+geom_boxplot()+
labs(x="Day",
y="Average Velocity",
title="Mean Velocity 3mo mice")+scale_fill_manual(values = c("dTg_ENR" = "#ad5fc9", "dTg_STD" = "#bc91cc","WT_ENR" = "#6eca64", "WT_STD" = "#98cc93","APPswe_ENR" = "#d6564b", "APPswe_STD" = "#db867f","PS1dE9_ENR" = "#918730", "PS1dE9_STD" = "#c5b740"))
mo14 %>%
mutate(mo14, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=mean.velocity, fill=Condition))+geom_boxplot()+
labs(x="Day",
y="Average Velocity",
title="Mean Velocity 13-14mo mice")+scale_fill_manual(values = c("dTg_ENR" = "#ad5fc9", "dTg_STD" = "#bc91cc","WT_ENR" = "#6eca64", "WT_STD" = "#98cc93","APPswe_ENR" = "#d6564b", "APPswe_STD" = "#db867f","PS1dE9_ENR" = "#918730", "PS1dE9_STD" = "#c5b740"))
mo25 %>%
mutate(mo25, `_Day`=fct_relevel(`_Day`,"1","2","3","4","5","6","7","8","9","10"))%>%
ggplot(aes(x=`_Day`,y=mean.velocity, fill=Condition))+geom_boxplot()+
labs(x="Day",
y="Average Velocity",
title="Mean Velocity 17-25mo mice")+scale_fill_manual(values = c("dTg_ENR" = "#6c7ed7", "dTg_STD" = "#909ef3","WT_ENR" = "#9f48a3", "WT_STD" = "#ce73cf","APPswe_ENR" = "#c85632", "APPswe_STD" = "#e9724b","PS1dE9_ENR" = "#9f9201", "PS1dE9_STD" = "#cab95b"))
back to top *** ## UPDATED Strategy (Thresholded) Graphs {#anchor17}
par(mfrow = c(2, 2))
Rtrack::plot_strategies(Rtrack::threshold_strategies(strategies, 0.4), experiment = experiment,
factor = "All", exclude.probe = TRUE)
back to top *** ## Probe Trial Number of Goals Crossings {#anchor18}
mo3.probe=filter(Results, Age_group=='3'&`_Day`=='7'&`_Trial`=='1')
mo14.probe=filter(Results, Age_group=='13-14'&`_Day`=='7'&`_Trial`=='1')
mo25.probe=filter(Results, Age_group=='17-25'&`_Day`=='7'&`_Trial`=='1')
mo3.probe %>%
ggplot(aes(x=`Strain`,y=goal.crossings, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Number of Goal Crossings",
title="Probe Trial Number of Goal Crossings 3mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
mo14.probe %>%
ggplot(aes(x=`Strain`,y=goal.crossings, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Number of Goal Crossings",
title="Probe Trial Number of Goal Crossings 13-14mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
mo25.probe %>%
ggplot(aes(x=`Strain`,y=goal.crossings, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Number of Goal Crossings",
title="Probe Trial Number of Goal Crossings 17-25mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
*** ## Probe Trial Number of former Goals Crossings {#anchor19}
mo3.probe=filter(Results, Age_group=='3'&`_Day`=='7'&`_Trial`=='1')
mo14.probe=filter(Results, Age_group=='13-14'&`_Day`=='7'&`_Trial`=='1')
mo25.probe=filter(Results, Age_group=='17-25'&`_Day`=='7'&`_Trial`=='1')
mo3.probe %>%
ggplot(aes(x=`Strain`,y=old.goal.crossings, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Number of old Goal Crossings",
title="Probe Trial Number of former Goal Crossings 3mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
mo14.probe %>%
ggplot(aes(x=`Strain`,y=old.goal.crossings, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Number of old Goal Crossings",
title="Probe Trial Number of former Goal Crossings 13-14mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
mo25.probe %>%
ggplot(aes(x=`Strain`,y=old.goal.crossings, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Number of Old Goal Crossings",
title="Probe Trial Number of former Goal Crossings 17-25mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
*** ## Probe Trial Time in old goal zone {#anchor20}
mo3.probe=filter(Results, Age_group=='3'&`_Day`=='7'&`_Trial`=='1')
mo14.probe=filter(Results, Age_group=='13-14'&`_Day`=='7'&`_Trial`=='1')
mo25.probe=filter(Results, Age_group=='17-25'&`_Day`=='7'&`_Trial`=='1')
mo3.probe %>%
ggplot(aes(x=`Strain`,y=time.in.zone.old.goal, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Time in s",
title="Probe Trial Time spent in old goal 3mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
mo14.probe %>%
ggplot(aes(x=`Strain`,y=old.goal.crossings, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Time in s",
title="Probe Trial Time spent in old goal 13-14mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
mo25.probe %>%
ggplot(aes(x=`Strain`,y=old.goal.crossings, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Time in s",
title="Probe Trial Time spent in old goal 17-25mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
*** ## Probe Trial Mean Initial heading error {#anchor21}
mo3.probe=filter(Results, Age_group=='3'&`_Day`=='7'&`_Trial`=='1')
mo14.probe=filter(Results, Age_group=='13-14'&`_Day`=='7'&`_Trial`=='1')
mo25.probe=filter(Results, Age_group=='17-25'&`_Day`=='7'&`_Trial`=='1')
mo3.probe %>%
ggplot(aes(x=`Strain`,y=`mean.initial.heading.error`, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Initial heading error in %??",
title="Probe Trial Mean Initial heading error 3mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
## Warning: Removed 4 rows containing non-finite values (stat_boxplot).
mo14.probe %>%
ggplot(aes(x=`Strain`,y=`mean.initial.heading.error`, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Initial heading error in %??",
title="Probe Trial Mean Initial heading error 13-14mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
mo25.probe %>%
ggplot(aes(x=`Strain`,y=`mean.initial.heading.error`, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Initial heading error in ???",
title="Probe Trial Mean Initial heading error 17-25mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).
# Testing Layout Options {.tabset .tabset-fade .tabset-pills} ***
mo3.probe %>%
ggplot(aes(x=`Strain`,y=`mean.initial.heading.error`, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Initial heading error in %??",
title="Probe Trial Mean Initial heading error 3mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
## Warning: Removed 4 rows containing non-finite values (stat_boxplot).
mo14.probe %>%
ggplot(aes(x=`Strain`,y=`mean.initial.heading.error`, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Initial heading error in %??",
title="Probe Trial Mean Initial heading error 13-14mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
mo25.probe %>%
ggplot(aes(x=`Strain`,y=`mean.initial.heading.error`, fill=Housing))+geom_boxplot()+
labs(x="Condition",
y="Initial heading error in ???",
title="Probe Trial Mean Initial heading error 17-25mo mice")+scale_fill_manual(values = c("ENR"="#b3669e","STD"="#98984d"))
## Warning: Removed 1 rows containing non-finite values (stat_boxplot).